摘要
常规的神经网络存在容易陷入局部极小点、收敛速度慢、泛化能力差的问题。为了解决这些问题,使用量子遗传算法来获得神经网络初始变量。由于其具有量子态和量子门操作,可有效提高寻优的性能,大大提高初始值的质量,为后续算法逼近全局最优奠定基础。在用该算法获得网络可变参数初值后,选择计算速度快的Levenberg-Marquardt算法对多层前馈神经网络的权值和阈值进行优化得到最优解,很好地解决了网络训练易收敛于局部极小点的问题。根据气体浓度和产气速率判断变压器是否故障,将无故障和有故障情况分别用各自的神经网络进行评估/诊断,分别给出变压器的健康水平和故障类型,有效减少了网络的复杂性,提高了训练和应用效果。将提出的方法应用于现场变压器的油中溶解气体分析,评估/诊断准确性达95%以上。
Conventional artificial neural networks （ANNs） still have problems of easy plunging into local minimum,slow convergence rate and bad generalization capacity.For solving these problems,a quantum genetic algorithm was used to obtain initial weight value and bias values of ANNs.The quantum state and quantum gate operation of the quantum algorithm could improve the optimization performance as well as the quality of the initial values effectively,which laid a foundation for global optimum approximation in the later algorithm.After then,Levenberg-Marquardt algorithm,a fast algorithm,was adopted to get the optimal solution by optimizing the weight value and the threshold of the multilayer feedforward ANNs.By doing that,the problem of local minimum convergence in the networks training was solved.Before evaluation or judging,gas concentration and gas generation speed were used to judge whether transformer fault,and the dissolved gas-in-oil data of normal transformers and fault transformer was used by individual ANNs,the former given transformer health level and the latter given transformer fault type.In this way,the complexity of the net was decreased prominently and the effect of training and applying were improved.The proposal algorithm is used to evaluate and diagnose on-site transformer based on dissolved gas-in-oil data,and the accuracy is higher than 95%.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2010年第30期121-127,共7页
Proceedings of the CSEE
